arXiv Machine Learning
Introducing the CZAR Loss: A Tailored Objective Function for Financial Log-Return Predictions
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Sep 23
Financially Guided Deep Portfolio Optimization
arXiv:2605.28853v2 Announce Type: replace-cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
arXiv Machine Learning
Jun 3
FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
arXiv AI
Aug 5
FinVerse: Financial Time-Series Benchmark
arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.
arXiv Machine Learning
Aug 18
Decision-Driven Regularization: A Blended Model for Learning and Optimization
arXiv:2608. 15124v1 Announce Type: new Abstract: In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features.
arXiv Computation and Language
4d ago
Can Language Models Learn to Forecast Stock Prices
arXiv:2609.36914v1 Announce Type: new Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning,...
arXiv AI
Aug 17
Forecast Collapse in Time-Series Foundation Models
arXiv:2608. 14106v1 Announce Type: cross Abstract: When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation.